A Visual Analytics Framework for Exploring Uncertainties in Reservoir Models

Sahaf, Zahra; Hamdi, Hamidreza; Mota, Roberta Cabral; Sousa, Mário Costa; Maurer, Frank

doi:10.5220/0006608500740084

Cited by 6 publications

(5 citation statements)

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“…Anupam and Tewar (2021) use PCA, t-SNE and k-means to select RMs. Zheng et al (2018) and Sahaf et al (2018Sahaf et al ( , 2019 apply MDS and k-means to represent the influence of production uncertainties and select RMs. Regarding risk curves, Silva et al (2020) used reorderable heatmaps to show the dissimilarities of risk curve pairs (e.g., area between risk curves).…”

Section: Related Workmentioning

confidence: 99%

Prototyping Visualizations as a Support for Selecting Representative Models of Petroleum Reservoirs

2023

Proceedings of the 17 Th International Conference on Interfaces and Human Computer Interaction 2023 (IHCI 2023), the 17th Inter

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Petroleum engineers usually create a set of hundreds of models of a given oil reservoir under analysis to represent its uncertainties. Assisted optimization approaches may help engineers to select a subset of these models (a.k.a. representative models, or RMs for short), which is used in computational flow simulations in replacement of the original set, aiming to reduce the total simulation runtime without changing the quality of these results. Despite the power of visualization techniques to help people to understand multidimensional datasets like the ones provided in this scenario, we noted a few efforts that use these techniques to help petroleum engineers to assist the selection of RMs. In this context, our research aims to test the hypothesis that it is possible to improve how interactive visualization resources are currently used to aid decision-making regarding the selection of RMs, mainly in the presence of multiple sets of RMs or multiple variables (obtained from running model simulations). This work presents our first steps towards this goal: (a) literature review and (b) definition of visualization prototypes that aim to help the analysis of RMs regarding the values of the variables provided by simulation outputs, and the risk curves associated with these variables. As preliminary results, we present our proposed interactive visualizations and briefly point out the design rationale behind these prototypes.

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Section: Related Workmentioning

confidence: 99%

Prototyping Visualizations as a Support for Selecting Representative Models of Petroleum Reservoirs

2023

Proceedings of the 17 Th International Conference on Interfaces and Human Computer Interaction 2023 (IHCI 2023), the 17th Inter

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“…Recently, multiple reservoir properties have been integrated to define static distance [14]. Facies, porosity, permeability, and water saturation for each grid are compared together using a special distance function.…”

Section: Combined Distancementioning

confidence: 99%

“…Other distance calculation formulas have been employed in previous studies [14,36,39,42,52]. For example, Patel et al [16] employed the Kantorovich distance to compare the probability distributions of the reservoir models.…”

Section: Datamentioning

confidence: 99%

“…If more than three datasets exist, an exact solution in 2D space does not exist but we numerically approximate the position. Since MDS is a simple but very powerful algorithm to manage the data, many studies used MDS to reduce the data dimension [2][3][4][6][7][8][9]14,15,18,20,21,26,[36][37][38][39][40][43][44][45]51,53]. MDS can maintain the distance information while reducing the dimension in a lower space.…”

Section: + ( − 3) =mentioning

confidence: 99%

“…Some researchers have attempted to extend their idea to 3D synthetic cases before applying to benchmark or real fields but the number of grids and generality remain limited [9,14,36]. Benchmark fields, such as PUNQ-S3 field, can be used to test a novel method to obtain field applicability [6,24,25].…”

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

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Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

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This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

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